Model comparison
o4-mini vs Qwen3.5 122B-A10B
o4-mini and Qwen3.5 122B-A10B score almost the same on the Noometry Index (41.6 vs 41.6), so choose on price, context window or the category you care about most.
Last verified . 23 shared benchmarks.
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. o4-mini scores higher in 6 categories and Qwen3.5 122B-A10B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.5 122B-A10B leads 60.2 to 54.2.
- The biggest single-benchmark swing is Mystery Game Puzzles: 5% for o4-mini and 17% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- Qwen3.5 122B-A10B accepts more context: 262K tokens versus 200K.
- Qwen3.5 122B-A10B has downloadable open weights; the other is API-only.
Side by side
| o4-mini | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.6 | 41.6 |
| Released | 2025-04-16 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 200K | 262K |
| Max output | 100K | 66K |
| Input $ / M tokens | $1.10 | $0.40 |
| Output $ / M tokens | $4.40 | $3.20 |
| Results tracked | 60 | 27 |
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Category by category
Coding o4-mini leads
o4-mini: 41.6 (#110), Qwen3.5 122B-A10B: 38.8 (#155)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Coding | 1369 | 1436 |
| SWE-bench Verified (bash only) | 45% | — |
| Aider Polyglot | 72% | — |
| LMArena WebDev | — | 1360 |
| SciCode | — | 35.6% |
| GSO | 3.6% | — |
| WeirdML | 52.6% | — |
| CadEval | 62% | — |
| ALE-Bench | 826.17 | — |
| AlgoTune | 1.72 | — |
Agentic & Tool Use Not comparable
o4-mini: 32.3 (#57), Qwen3.5 122B-A10B: —
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 53.2% | — |
| GDPval | 25.3% | — |
| METR Time Horizons | 63.9% | — |
Reasoning Qwen3.5 122B-A10B leads
o4-mini: 24.6 (#143), Qwen3.5 122B-A10B: 26.4 (#121)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| CritPt | 0.6% | 0.9% |
| LMArena Hard Prompts | 1351 | 1421 |
| Mystery Game Puzzles | 5% | 17% |
| DTBench | 77.6% | 84.3% |
| LMCA | 26.5% | 32.2% |
| ARC-AGI-2 | 6.1% | — |
| SimpleBench | 38.7% | — |
| Kagi LLM Benchmark | 67.6% | — |
| NYT Connections (extended) | — | 51.7% |
| ARC-AGI-1 | 58.7% | — |
| Chess Puzzles | 26% | — |
| EnigmaEval | 9.2% | — |
| Thematic Generalization | — | 51.2% |
| Epoch Capabilities Index | 145.63 | — |
| ForecastBench | 61.8 | — |
Math o4-mini leads
o4-mini: 40.2 (#79), Qwen3.5 122B-A10B: 36.4 (#114)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1390 | 1432 |
| FrontierMath (Tiers 1-3) | 36.1% | — |
| FrontierMath Tier 4 | 4.9% | — |
| OTIS Mock AIME 2024-2025 | 81.7% | — |
| Omni-MATH | 72% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 24.8% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge o4-mini leads
o4-mini: 43.5 (#83), Qwen3.5 122B-A10B: 38.2 (#138)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| Vectara Hallucination Rate | 18.6% | 11.2% |
| LMArena Expert | 1344 | 1431 |
| GPQA Diamond | 79.6% | — |
| Humanity's Last Exam | 18.1% | — |
| SimpleQA Verified | 19.6% | — |
| MMLU-Pro | 82% | — |
| Confabulations | 15.8% | — |
| GPQA (HELM) | 73.5% | — |
Multimodal Too close to call
o4-mini: 39.5 (#46), Qwen3.5 122B-A10B: 39.0 (#52)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | 1194 | 1245 |
| GeoBench | 64% | — |
| VPCT | 57.5% | — |
Multilingual Qwen3.5 122B-A10B leads
o4-mini: 47.2 (#149), Qwen3.5 122B-A10B: 51.8 (#101)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1336 | 1400 |
| LMArena Chinese | 1355 | 1462 |
| LMArena French | 1362 | 1442 |
| LMArena German | 1336 | 1425 |
| LMArena Japanese | 1308 | 1364 |
| LMArena Korean | 1313 | 1353 |
| LMArena Russian | 1334 | 1400 |
| LMArena Spanish | 1346 | 1423 |
Instruction Following o4-mini leads
o4-mini: 75.4 (#63), Qwen3.5 122B-A10B: 74.0 (#109)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1321 | 1399 |
| IFEval | 92.8% | — |
Long Context o4-mini leads
o4-mini: 45.5 (#31), Qwen3.5 122B-A10B: 43.0 (#105)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1315 | 1410 |
| Fiction.LiveBench | 77.8% | — |
Writing & Preference Qwen3.5 122B-A10B leads
o4-mini: 54.2 (#148), Qwen3.5 122B-A10B: 60.2 (#98)
| Benchmark | o4-mini | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1353 | 1417 |
| LMArena Creative Writing | 1294 | 1369 |
| LMArena Multi-Turn | 1350 | 1416 |
| Short-Story Creative Writing | 75% | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is o4-mini better than Qwen3.5 122B-A10B?
o4-mini and Qwen3.5 122B-A10B score almost the same on the Noometry Index (41.6 vs 41.6), so choose on price, context window or the category you care about most.
Which is cheaper, o4-mini or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is o4-mini or Qwen3.5 122B-A10B better for coding?
o4-mini scores higher on coding benchmarks: 41.6 versus 38.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5 122B-A10B does, with 262K tokens against 200K.
How many benchmarks do o4-mini and Qwen3.5 122B-A10B share?
23 benchmarks have published results for both models. o4-mini has 60 scored results on Noometry and Qwen3.5 122B-A10B has 27.